3.1. Protein characterization
This study successfully determined the plasma protein maps of patients with dyslipidemia and normal controls. A total of 218,812 plasma protein maps were obtained, 25,296 of which were matched with peptide segments. The total number of peptides was 4,170, and 601 proteins were identified. Detailed information on mass spectrometry collection and identification is shown in Table 2.
Table 2
Protein identification information
Database | Number of spectra | Number of peptide spectra | Number of peptides | Number of protein |
Homo sapiens | 218812 | 25296 | 4107 | 601 |
3.2. Identification of differentially expressed proteins
The identification of differentially expressed proteins was based on the fold change(FC > 1.2 or FC < 0.83, P value obtained by T test < 0.05)in expression between the normal group and the dyslipidemia group. The statistical results are shown in Table 3. A total of 137 differentially expressed proteins were identified between the normal group and the dyslipidemia group. Of these, 60 proteins were upregulated and 77 proteins were downregulated. The Volcano Plot in Fig. 1 summarizes these results. The x-coordinate represents the fold change difference (transformed with base 2 log), and the y-coordinate represents the p-value significance of the difference (transformed with base 10 log).
Table 3
Differentially expressed proteins between the normal group and the dyslipidemia group
Group name | Number of protein | Up-regulated protein | Down-regulated protein |
normal group VS Dyslipidemia group | 137 | 60 | 77 |
3.3. Cluster analysis of differentially expressed proteins
A hierarchical cluster algorithm was used to analyze the differentially expressed proteins and a heat map was drawn, as shown in Fig. 2. The y-coordinate represents significantly differentially expressed proteins, and the x-coordinate shows sample information. Differentially expressed proteins in the heat map are shown in different colors based on the amounts expressed in different samples (transformed with base 2 log). We observed that the protein expression profiles clearly fell into two categories, indicating that patients with dyslipidemia and normal subjects have significantly different plasma protein expression patterns.
3.4. Bioinformatics analysis
Fisher's test was used to analyze the GO function of proteins which were differentially expressed between the normal group and the dyslipidemia group, and the results are shown in Fig. 3A. The x-coordinate in this figure indicates the enriched GO functional classification, which is divided into three categories: Biological Process (BP), Cellular Component (CC) and Molecular Function (MF). The figure shows the top 10 enriched items in the BP, CC, and MF categories. The items in each category are ordered from left to right depending on their p value: the more to the left, the more significant their p value. The left ordinate represents the percentage of the corresponding proteins or genes with respect to the total number of proteins or genes, while the right ordinate represents the number of corresponding proteins or genes. Analysis showed significant changes in several Biological Processes (BP), including protein activation cascades, adaptive immune responses, complement activation, and regulation of acute responses, among others. There were also significant changes in Cellular Component (CC) items, including extracellular region, extracellular space, extracellular region part, blood microparticle and extracellular exosome, among others. Molecular Function (MF) showed significant changes in antigen binding, serine-type endopeptidase activity, endopeptidase inhibitor activity, endopeptidase regulator activity and peptidase inhibitor activity.
Fisher's test was used to conduct KEGG pathway enrichment analyses for differentially expressed proteins between the normal group and the dyslipidemia group, and the results are shown in Fig. 3B. The y ordinate represents the KEGG pathway that is enriched with the differentially expressed protein. The x ordinate represents the enrichment factor (Rich Factor ≤ 1) for the enriched KEGG pathway, and this represents the ratio of the number of differentially expressed proteins annotated in this KEGG pathway term to the total number of proteins annotated in this pathway term. The color gradient in the bubble represents the size of the P value, with the color fading from green to red. The closer the color is to red, the smaller the P value, and the higher the significance level of the corresponding KEGG pathway, indicating that this KEGG pathway has a higher correlation with dyslipidemia. The size of the bubble indicates the number of differentially expressed proteins enriched in the KEGG pathway. This analysis showed that pathways which were significantly altered included Complement and coagulation cascades, Staphylococcus aureus infection, Osteoclast differentiation, Systemic Lupus erythematosus, Phagosome, Leishmaniasis, Natural killer cell mediated cytotoxicity and Tuberculosis, among others.
3.5. Protein-protein interaction network analysis (PPI)
A protein interaction network diagram was constructed for differentially expressed proteins between the normal and disease groups (Fig. 4). Nodes in Fig. 4 represent proteins, and lines represent interactions between proteins. Yellow nodes in the network represent differentially expressed proteins, whereas blue nodes represent other proteins included in the interaction network database which can interact directly with the differentially expressed proteins. This analysis revealed that dyslipidemia was highly associated with protein interactions related to complement, immunity, inflammation, coagulation, hemostasis, lipid metabolism, oxidation and anti- oxidation. The main proteins involved in immune and inflammatory responses were complement C4-B (C4B), immunoglobulin lambda-like polypeptide 5 (IGLL5), immunoglobulin iodine chain (VPREB1), low affinity immunoglobulin gamma Fc region receptor III-A (FCGR3A), and complement factor H-related protein 1 (CFHR1). The proteins involved in coagulation and hemostasis were: fibrinogen-like protein 1 (FGL1), coagulation factor XII (F12), histidine-rich glycoprotein (HRG), alpha-2-macroglobulin (A2M), and vitamin D-binding protein (GC). The proteins involved in lipid metabolism were: apolipoprotein C-IV (APOC4), apolipoprotein F (APOF), apolipoprotein D (APOD), apolipoprotein E (APOE), and apolipoprotein (a) (LPA). In terms of oxidation and anti-oxidation, the proteins were alpha-aminoadipic semialdehyde dehydrogenase (ALDH7AA), heat-stable enterotoxin receptor (GUCY2C) and adenylate cyclase type 6 (ADCY6).
3.6. Verification of metabolomics methodology
QC samples were interspersed in the procedure with the purpose of monitoring the operation status of the instrument and verifying the repeatability of the analytical method. Figure 5A shows an ion flow diagram of 7 QC samples, to monitor the reproducibility of the methodology. Figure 5B shows the RSD values at all ionic strengths in the QC group. RSD < 20% for more than 85% of compounds.
3.7. Metabolic profile analysis
Compound Discoverer software was used for multi-dimensional statistical analysis of metabolite data. A PCA score graph was constructed to observe the differences in the metabolic profiles between normal and dyslipidemia patients. The PCA score (Fig. 6) diagram clearly shows that QC samples cluster together, which proves that the instrument was in a stable state during the entire sample collection process. Figure 6 also shows that although there is partial overlap between the normal and dyslipidemia groups, there is an overall trend towards separation, which may be due to uncontrollable factors, which are normal phenomena in clinical sample analysis.
3.8. Screening and identification of potential biomarkers for dyslipidemia
Differences in plasma metabolites between the normal population and patients with dyslipidemia were identified by metabolic profile analysis. The Volcano Plot constructed with Compound Discoverer software, combined with S-plot analysis software, allows quick screening of differentially expressed metabolites. Table 4 shows 69 differentially expressed metabolites identified based on P ≤ 0.05 and FC ≥ 1.5. Results mainly show a significant increase in ester levels and a decrease in the levels of some indoles. The heatmap in Fig. 7 shows changes in metabolites in each individual, reflecting the differing trends in metabolite expression between patients with dyslipidemia and the normal population. Red represents an increasing trend in metabolite levels, green represents a decreasing trend in metabolite levels, and the color brightness reflects the degree of change. Interestingly, the normal and disease groups were roughly divided into two. In addition, indole-3-propionic acid (IPA) levels were significantly reduced in the disease group while ester levels were generally upregulated in the disease group.
Table 4
Identification of potential biomarkers in human plasma samples, based on analysis of healthy subjects and dyslipidemia patients.
NO | Retention Time | Name | formula | Molecular Weight | Ion Mode | Levels |
P1 | 6.56 | Indole-3-propionic acid | C11H11NO2 | 189.0793 | [M-H]- | ↓ |
P2 | 3.97 | Indole-3-acrylic acid | C11H9NO2 | 187.0631 | [M + H]+ | ↓ |
P3 | 7.13 | 4-ethylphenylsulfonic acid | C8H10O4S | 202.0302 | [M-H]- | ↓ |
P4 | 6.56 | Indole-3-propionic acid | C11H11NO2 | 189.0793 | [M + H]+ | ↓ |
P5 | 9.63 | Bilirubin | C33H36N4O6 | 584.2631 | [M + H]+ | ↓ |
P6 | 10.78 | Deoxycholic acid | C24H40O4 | 392.2934 | [M-H]- | ↓ |
P7 | 12.52 | DG (42:5) | C45H78O5 | 698.5881 | [M + H]+ | ↓ |
P8 | 11.48 | MG (24:5) | C27H44O4 | 432.3249 | [M-H]- | ↓ |
P9 | 12.53 | DG (22:2_22:6) | C47H76O5 | 720.5699 | [M + H]+ | ↓ |
P10 | 11.36 | LPS (22:6) | C28H44NO9P | 569.2745 | [M-H]- | ↑ |
P11 | 11.53 | LPS (26:4) | C32H56NO9P | 629.3710 | [M-H]- | ↑ |
P12 | 2.65 | CAR (8:1(OH)) | C15H27NO5 | 301.1894 | [M-H]- | ↑ |
P13 | 11.22 | LPS (20:0) | C26H52NO9P | 553.3395 | [M-H]- | ↑ |
P14 | 11.50 | LPE (22:5) | C27H46NO7P | 527.3025 | [M-H]- | ↑ |
P15 | 11.80 | LPS (26:3) | C32H58NO9P | 631.3867 | [M-H]- | ↑ |
P16 | 11.50 | LPE (22:5) | C27H46NO7P | 527.3025 | [M + H]+ | ↑ |
P17 | 11.58 | LPE (20:3) | C25H46NO7P | 503.3024 | [M-H]- | ↑ |
P18 | 5.34 | CAR (10:0(6Ke)) | C17H31NO5 | 329.2199 | [M + H]+ | ↑ |
P19 | 11.49 | LPS (24:2) | C30H56NO9P | 605.3709 | [M-H]- | ↑ |
P20 | 11.09 | FA (20:3) | C20H32O3 | 320.2357 | [M-H]- | ↑ |
P21 | 11.36 | LPS (26:5) | C32H54NO9P | 627.3554 | [M-H]- | ↑ |
P22 | 11.33 | LPE (22:6) | C27H44NO7P | 525.2868 | [M-H]- | ↑ |
P23 | 11.25 | 16,17-epoxy-DHA | C22H30O3 | 342.2201 | [M-H]- | ↑ |
P24 | 11.75 | 1-Palmitoyl-2-sn-glycero-3-phosphatidylcholine | C32H58NO11P | 663.3742 | [M-H]- | ↑ |
P25 | 11.89 | LPS (24:1) | C30H58NO9 P | 607.3867 | [M-H]- | ↑ |
P26 | 2.95 | 12-Aminododecanoic acid | C12H25NO2 | 215.1883 | [M + H]+ | ↑ |
P27 | 11.39 | PS (O-18:0/18:2) | C42H80NO9 P | 773.5563 | [M + H]+ | ↑ |
P28 | 10.23 | LPA (14:0) | C17H35O7P | 382.2119 | [M + H]+ | ↑ |
P29 | 10.75 | LPE (P-18:1)/LPE (O-18:2) | C23H46NO6 P | 463.3058 | [M + H]+ | ↑ |
P30 | 11.70 | SM (d18:2/14:0) | C37H73N2O6P | 672.5200 | [M + H]+ | ↑ |
P31 | 11.53 | LPS (26:4) | C32H56NO9P | 629.3710 | [M + H]+ | ↑ |
P32 | 12.25 | SM (d17:1/18:3) | C40H75N2O6P | 710.5333 | [M + H]+ | ↑ |
P33 | 11.84 | LPS(O-20:0) | C26H54NO8P | 539.3601 | [M-H]- | ↑ |
P34 | 11.75 | 1-Palmitoyl-2-(5-keto-6-octendioyl)-sn-glycero-3-phosphatidylcholine | C32H58NO11P | 663.3742 | [M + H]+ | ↑ |
P35 | 12.19 | LPE(P-18:0)/LPE(O-18:1) | C23H48NO6P | 465.3226 | [M-H]- | ↑ |
P36 | 11.06 | Arachidonic acid methyl ester | C21H34O2 | 318.2556 | [M + H]+ | ↑ |
P37 | 12.38 | N-Palmitoylsphingosine | C34H67NO3 | 537.5117 | [M + H]+ | ↑ |
P38 | 10.40 | 16(R)-HETE | C20H32O3 | 342.2193 | [M + H]+ | ↑ |
P39 | 10.75 | CAR (18:1(11E)) | C25H47NO4 | 425.3498 | [M + H]+ | ↑ |
P40 | 10.72 | LPC (22:4) | C30H54NO7P | 571.3632 | [M + H]+ | ↑ |
P41 | 10.75 | LPE(P-18:1)/LPE(O-18:2) | C23H46NO6P | 463.3058 | [M + H]+ | ↑ |
P42 | 10.7 | LPE (P-16:0)/LPE(O-16:1) | C21H44NO6P | 437.2902 | [M + H]+ | ↑ |
P43 | 11.38 | LPS (24:3) | C30H54NO9P | 603.3554 | [M-H]- | ↑ |
P44 | 13.71 | SM (d18:0/22:3) | C45H85N2O6P | 780.6112 | [M + H]+ | ↑ |
P45 | 10.92 | LPE(P-18:0) | C23H48NO6P | 465.3215 | [M + H]+ | ↑ |
P46 | 11.33 | LPE (22:6) | C27H44NO7P | 525.2868 | [M-H]- | ↑ |
P47 | 11.58 | Glycerophospho-N-palmitoyl ethanolamine | C21H44NO7P | 453.2865 | [M-H]- | ↑ |
P48 | 10.79 | LPC (20:2) | C28H54NO7P | 547.3634 | [M + H]+ | ↑ |
P49 | 13.81 | PC (16:0_22:4) | C46H84NO8P | 809.5900 | [M + H]+ | ↑ |
P50 | 11.36 | LPS (26:5) | C32H54NO9P | 627.3554 | [M-H]- | ↑ |
P51 | 11.22 | LPS (20:0) | C26H52NO9P | 553.3395 | [M-H]- | ↑ |
P52 | 11.29 | LPS (22:1) | C28H54NO9P | 579.3552 | [M-H]- | ↑ |
P53 | 13.47 | PC (18:0_22:6) | C48H84NO8P | 833.5906 | [M + H]+ | ↑ |
P54 | 12.10 | SM(d18:1/18:4) | C41H75N2O6P | 722.5327 | [M + H]+ | ↑ |
P55 | 6.70 | 9-Oxononanoicacid | C9H16O3 | 172.1101 | [M-H]- | ↑ |
P56 | 12.25 | SM(d16:1/17:0) | C38H77N2O6P | 688.5514 | [M + H]+ | ↑ |
P57 | 13.02 | DL-Dipalmitoylphosphatidylcholine | C40H80NO8P | 733.5616 | [M + H]+ | ↑ |
P58 | 13.71 | SM(d16:1/22:0) | C43H87N2O6P | 758.6295 | [M + H]+ | ↑ |
P59 | 11.41 | MG (26:4) | C29H50O4 | 462.3706 | [M + H]+ | ↑ |
P60 | 12.03 | SM(d16:0/16:1) | C37H75N2O6P | 674.5357 | [M + H]+ | ↑ |
P61 | 12.25 | N-Palmitoyl taurine | C18H37NO4S | 726.4982 | [M + H]+ | ↑ |
P62 | 11.38 | LPS (24:3) | C30H54NO9P | 603.3554 | [M-H]- | ↑ |
P63 | 13.02 | SM(d18:1/18:0) | C41H83N2O6P | 730.5984 | [M + H]+ | ↑ |
P64 | 11.75 | LPS (22:0) | C28H56NO9P | 581.3708 | [M-H]- | ↑ |
P65 | 10.65 | LPC (20:3) | C28H52NO7P | 545.3473 | [M + H]+ | ↑ |
P66 | 10.91 | Linoleic acid-biotin | C28H48N4O3S | 1084.6735 | [M + H]+ | ↑ |
P67 | 10.55 | LPC (20:4) | C28H50NO7P | 543.3320 | [M + H]+ | ↑ |
P68 | 10.91 | LPC (18:0) | C26H54NO7P | 523.3633 | [M + H]+ | ↑ |
P69 | 10.67 | 1-Palmitoylglycerophosphocholine | C24H50NO7P | 495.3319 | [M + H]+ | ↑ |
3.9. Analysis of metabolic pathways in dyslipidemia
Figure 8 shows the enrichment pathway results, based on MetaboAnalyst and KEGG common platforms for the analysis of enrichment of potential biomarkers and topological analysis. Differences between patients with dyslipidemia and the normal population reside mainly in glycerolipid, sphingolipid, porphyrin, alpha-linolenic acid, linoleic acid and arachidonic acid metabolism.